Current speech recognition systems require a training session for a user. In the currently available speech recognition technology, a well trained speaker-independent model yields an accuracy of 90-93%. Improving the accuracy from 93% to 98% is challenging.
The speech enabled cockpit is designed to recognize the voices of the pilot and co-pilot in the cockpit. Extensive research has been carried out to enable and improve speech recognition in the cockpit of an aircraft, which typically has a high level of background noise. There are currently two methods to improve accuracy of the speech recognition technology in the cockpit. One method uses offline training for every pilot and co-pilot. This is a costly, laborious, time intensive process that requires a pilot to dedicate many hours to take a tutorial and create a personal voice profile. Another method implements real-time training as the pilot, sitting in the cockpit, uses the speech recognition system before and during a flight.
This latter training technique introduces latency and often requires additional processing and memory. In this latter training technique, during the voice practice, there is often background noise inside the cockpit and/or outside the aircraft environment. Currently available systems correct for some of the background noise from the cockpit when the pilot is initiating a voice command with the on-board systems. However, the current technology for noise correction still has several problems.